Fraud Detection Using Machine Learning Methods

Fraud is an ever-present challenge, and the fight against it is ongoing. The tools and techniques used by both fraudsters and those trying to stop them are constantly evolving. This article explores how machine learning (ML) can be used to detect fraud, examines popular algorithms, and shares best practices for maximizing effectiveness.

The Evolution of Fraud Detection

Traditionally, fraud detection relied on rules-based systems that used predefined criteria to flag potentially fraudulent activity. Machine learning enhances these methods by analyzing historical data to identify patterns and adapt to evolving threats. This makes ML models ideal for tackling today’s dynamic fraud tactics.

Machine Learning Defined

Before diving into the specifics of applying machine learning to fraud detection, it's important to understand what machine learning is. Machine learning is a subfield of AI that focuses on developing algorithms and models that give computers the ability to learn from data, identify patterns within the data, and make decisions based on their learnings.

There are three main types of machine learning:

Supervised learning: A computer is taught to make predictions or decisions based on examples. The algorithm is given a dataset with both the input data (problems) and the correct output (answers). The algorithm studies this dataset and learns the relationship between the input and output.

Read also: Preventing Educational Fraud
Unsupervised learning: A computer learns to identify patterns or structures in data without being given any specific examples or correct answers. The algorithm is given a dataset with only input data, without any corresponding correct outputs (answers). The algorithm’s job is to analyze this data and discover underlying patterns.
Reinforcement learning: A computer learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The algorithm explores an environment and makes decisions. For each decision that it makes, it receives feedback as either a reward or a penalty. The algorithm’s goal is to learn the best strategy, or policy, to make decisions that maximize its cumulative rewards over time.

Approaches to Fraud Detection with Machine Learning

When using machine learning algorithms for fraud detection, there are generally two approaches: anomaly detection and classification.

Anomaly Detection

Anomaly detection, also called clustering, is a machine learning technique used to identify unusual behavior. Far-out data points that indicate unusual behavior are referred to as point anomalies. In financial fraud detection, most financial transactions (more than 99 percent) are not fraudulent. Hence, the small percentage of transactions that fraudsters actually do perpetrate are point anomalies. This approach falls under unsupervised learning. Unsupervised anomaly detection techniques are used to fill in the gaps where supervised training models might be lacking. These techniques empower AI models to recognize previously unpredicted-but still unusual-behavior patterns.

Classification

Using classification in machine learning to detect fraud approaches the problem from a different angle. Here, you train a model to learn the characteristics of good and bad transactions in order to classify new transactions coming in. This approach falls under supervised learning. The system learns by reviewing numerous records and labeling the data as either fraudulent or a typical transaction. By examining these examples, the model learns how to spot and classify fraud based on previously observed patterns.

Read also: Mastering Anomaly Detection

Machine Learning Algorithms for Fraud Detection

Many machine learning algorithms can be used for fraud detection. However, there is no single "best" algorithm, as the optimal choice depends on the data available.

Logistic Regression: This is the most basic yet powerful machine learning algorithm you can use to predict true or false (binary) values.
Decision Trees: Decision trees are another popular algorithm that learns rules to split or classify data. The model is a set of rules that’s easy to explain, and these rules can be used to create a rules-based system.
Random Forests: A random forest is a machine learning algorithm that builds on multiple decision trees to provide classifications that are more accurate. It averages the results of individual decision trees, making its predictive power superior. However, random forests are less explainable than decision trees, as they result in many rules instead of a single set.
K-Nearest Neighbors (KNN): This is a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k best neighbors. It makes use of a distance function like the Euclidean distance. The training process does not exactly produce a model.
K-Means Clustering: This is an unsupervised machine learning algorithm that solves clustering problems. The algorithm works by grouping a given dataset into a number of clusters such that data points in a cluster are as similar as possible.
Graph Neural Networks (GNN): Specific types of AI, known as graph neural networks (GNN), are designed to process data that can be represented as a graph, such as the data very common to the banking industry.
Long Short-Term Memory (LSTM): Using advanced, long short-term memory (LSTM) AI models, American Express was able to improve fraud detection by 6%.

Challenges and Considerations

Unbalanced Datasets

In real-world fraud detection, it’s almost guaranteed that you’re going to have to deal with an unbalanced dataset. This is because fraud entries are a small minority. This is a problem if you’re applying supervised machine learning because the algorithms work best with balanced data.

Evolving Fraud Tactics

It’s really a cat-and-mouse game when dealing with fraudsters. Their behavior quickly changes, which leads to changes in the data as well. This means that it’s important to constantly train new fraud detection models. Advanced, self-optimizing technologies can even automatically retrain both supervised and unsupervised models.

The Need for Explainability

Explaining what a machine learning system is doing is critical; this is often referred to as “white boxing.” Machine learning methods and models are generally black boxes. It’s very difficult (if not impossible) to explain to analysts why they got the score or decision that they received. There are many approaches to making fraud analytics interpretable, including scorecards based on local linear approximation, generation of textual narratives, and generation of graphical data visualizations. Instead, prioritize platforms offering “whitebox processing” that provide clear, human-understandable explanations for their risk scores, like highlighting the key factors influencing a decision.

Model Monitoring and Adaptation

All things change, and your fraud analytics must adapt over time. Ongoing monitoring of machine learning fraud detection systems is imperative for success. As populations and the underlying data shift, expected system inputs degrade and therefore have an impact on overall performance. Newer machine learning methods can adapt to new and unidentified patterns as underlying changes occur. A good monitoring program is based on a proactive approach.

Data Requirements

AI models require extremely large amounts of data to train, learn and grow. This data must be either sourced or created (synthetic data), but also curated. The adage that more data equals better models is true when it comes to fraud detection. Practitioners need their machine learning platform to scale as data and complexity increase.

The Benefits of Machine Learning vs Traditional Fraud Detection

Machine learning technology includes several essential advantages over traditional, rules-based systems. These include:

Feature	Machine Learning	Traditional Fraud Detection
Detection Speed	Real-time, instant alerts	Often delayed, batch-based
Accuracy	High, adapts rapidly to new threats	Lower, frequently misses novel fraud, can be avoided by criminals
False Positives	Can understand normal patterns, allowing customers to transact without interruption	Static rules can result in false positives, leading to annoyed customers
Adaptability	Learns from new data	Relies on manual updates
Cost Efficiency	Reduces manual reviews	Labor-intensive, requiring frequent upkeep
Regulatory Compliance	Easier audit trails	Can be less transparent

How Businesses are Implementing Machine Learning for Fraud Detection

Machine learning offers substantial advantages across various sectors, including financial services, retail, eCommerce, and insurance. Its key strengths lie in its capacity to swiftly analyze vast datasets, build profiles of typical customer activities, and readily adjust to emerging trends and patterns.

Financial Institutions: Feedzai’s research shows that 90% of global banks are already utilizing AI and machine learning for fraud prevention and detection. The top use cases for AI and machine learning include scam prevention, transaction fraud detection, AML transaction monitoring, identity verification, and customer journey optimization.
Retail Sector: Major retailers, including industry giants like Walmart, are integrating machine learning-powered real-time video analytics into their loss prevention strategies. This technology has proven effective in substantially reducing inventory shrinkage.
ECommerce Businesses: Machine learning models are essential for enabling eCommerce merchants to prevent fraudulent transactions and ensure that legitimate shoppers can make purchases without unnecessary friction.
Insurance and Healthcare Industries: Machine learning algorithms can identify fraudulent healthcare claims with a high degree of accuracy.

Examples of Machine Learning for Fraud Detection

Businesses that deal with customer payments can apply machine learning-based fraud detection and prevention for different payment scenarios:

In-person payments:
- Credit card fraud detection: Machine-learning algorithms can analyze transaction data (e.g. time, location, amount, and business) to identify patterns and flag potentially fraudulent transactions in real time.
- Point-of-sale (POS) anomaly detection: Machine learning can monitor POS transactions and identify unusual patterns.
Mobile payments:
- Device fingerprinting: Machine-learning models can analyze device-specific information (e.g. device model, operating system, IP address) to create a unique "fingerprint" for each user.
- Behavioural biometrics: Machine learning can analyze user behaviour patterns, such as typing speed, swipe gestures, or app usage, to verify the user’s identity and detect any anomalies that may suggest fraud.
E-commerce:
- Account takeover prevention: Machine learning can monitor user login patterns and detect unusual activities, such as multiple failed login attempts or login attempts from new devices or locations.
- Friendly fraud detection: Machine learning can identify patterns related to friendly fraud, in which customers make a purchase and later claim that the transaction was unauthorised or that they never received the product.

5 Steps to Choosing a Machine Learning Platform for Fraud

Step 1: Ensure the Platform is Future-Proof and Flexible: You need a flexible system that can adapt to new banking channels, client needs, and changing fraud patterns, while handling increased data volumes.
Step 2: Demand Control and Customization: You should have complete control to adapt to new fraud trends by easily introducing new or custom models without constant vendor approval.
Step 3: Prioritize Transparency and Explainability: Prioritize platforms offering “whitebox processing” that provide clear, human-understandable explanations for their risk scores.
Step 4: Guarantee Scalability and Integration: The system must be scalable, handling increased transaction volumes and new business use cases with resilience.
Step 5: Look for a Partnership and Commitment to Responsible AI: A good partner will also commit to Responsible AI frameworks, such as TRUST (Transparency, Robustness, Unbiased, Security, and Testing).

The Rise of AI in Fraud Detection

With the advancement of faster payment services and technologies like GenAI and deepfakes, traditional methods of fraud detection are no longer sufficient. AI and machine learning solutions are crucial tools for fraud detection and prevention. Research firms project substantial growth in the AI in Fraud Detection market. AI technology allows computers to behave, learn, adapt, problem solve, and act with autonomy in ways similar to human cognition.

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